July 2018
Volume 59, Issue 9
Open Access
ARVO Annual Meeting Abstract  |   July 2018
Stability Analysis of Lamina Cribrosa Structure in Repeated Optical Coherence Tomography Scans
Author Affiliations & Notes
  • James Fishbaugh
    Computer Science and Engineering, NYU Tandon School of Engineering, Brooklyn, New York, United States
  • Sungmin Hong
    Computer Science and Engineering, NYU Tandon School of Engineering, Brooklyn, New York, United States
  • Hiroshi Ishikawa
    NYU Langone Eye Center, NYU School of Medicine, New York, New York, United States
  • Mathilde Ravier
    Computer Science and Engineering, NYU Tandon School of Engineering, Brooklyn, New York, United States
  • Gadi Wollstein
    NYU Langone Eye Center, NYU School of Medicine, New York, New York, United States
  • Joel S Schuman
    NYU Langone Eye Center, NYU School of Medicine, New York, New York, United States
  • Guido Gerig
    Computer Science and Engineering, NYU Tandon School of Engineering, Brooklyn, New York, United States
  • Footnotes
    Commercial Relationships   James Fishbaugh, None; Sungmin Hong, None; Hiroshi Ishikawa, None; Mathilde Ravier, None; Gadi Wollstein, None; Joel Schuman, Zeiss (P); Guido Gerig, None
  • Footnotes
    Support   NIH R01-EY013178, NIH R01-EY025011, and New York State Center for Advanced Technology in Telecommunications (CATT)
Investigative Ophthalmology & Visual Science July 2018, Vol.59, 2103. doi:
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    • Get Citation

      James Fishbaugh, Sungmin Hong, Hiroshi Ishikawa, Mathilde Ravier, Gadi Wollstein, Joel S Schuman, Guido Gerig; Stability Analysis of Lamina Cribrosa Structure in Repeated Optical Coherence Tomography Scans. Invest. Ophthalmol. Vis. Sci. 2018;59(9):2103.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : Dynamics of lamina cribrosa (LC) responding to intraocular pressure (IOP) changes are thought to contribute to glaucoma pathogenesis. In order to extract meaningful biomarkers, the effect of pressure on the LC must be readily distinguished from the inherent variability due to scanning conditions, image noise, registration, and segmentation. The purpose of this study is to assess the stability of our new 3D LC micro-structure analysis method using repeated 3D OCT scans on a healthy volunteer.

Methods : Ten 3D OCT images were acquired from a healthy volunteer scanned with a prototype swept source OCT system. A geometric average template image was estimated by minimizing the amount of deformation which map all images into correspondence. The non-linear deformations (Fig A) encode structural differences between scans. Additionally, 45 templates were estimated using unique image pairs (90 images), as only 2 images are typically acquired. Segmentations of LC beams and pores using multiscale vessel enhancement was done in template space and mapped to observations via deformations. Another volunteer was scanned under elevated IOP using an ophthalmodynamometer.

Results : Deformations from template to each image (Fig B) show low magnitude (mean ~1 voxel) and consistent distribution, indicating that each imaged LC is structurally similar. Templates from 2 scans (Fig C) are nearly indistinguishable from the template estimated with 10 scans, showing stability with limited images. This is quantified in Fig D; the deformation between templates and repeated observations (0.85±0.5 voxels) is an order of magnitude lower than a patient with artificially elevated IOP (p<0.0001 Wilcoxon signed-rank). Segmented LC beams and pores show a max volume difference of 5.8% and 63 of 90 images are within 2.9% (Dice overlap 0.98±0.006).

Conclusions : The variability of the LC structure due to imaging and deformable modeling is clearly delineated from the effect of increased IOP. Templates estimated from image pairs show measured LC structural differences are minimal (< 1 voxel) and that 2 scans are sufficient.

This is an abstract that was submitted for the 2018 ARVO Annual Meeting, held in Honolulu, Hawaii, April 29 - May 3, 2018.

 

A) Geometric average template encodes structural differences between scans. B) Distributions of deformation between template and 10 repeated scans. C) Templates estimated from 2 and 10 images are highly similar. D) Deformations between repeated scans are an order of magnitude lower than those of a subject under elevated IOP.

A) Geometric average template encodes structural differences between scans. B) Distributions of deformation between template and 10 repeated scans. C) Templates estimated from 2 and 10 images are highly similar. D) Deformations between repeated scans are an order of magnitude lower than those of a subject under elevated IOP.

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